{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `Geopandas`两个重点\n",
    "1. `GeoSeries`: 在`Pandas`中`Series`表示列，`Geopandas`中`GeoSeries`特指表示空间数据的列\n",
    "2. `GeoDataFrame`相当于一个二维表，表示多个属于同一类别空间要素的集合，在GIS被称为“图层”\n",
    "\n",
    "![GeoDataFrame-GeoSeries.png](./imgs/GeoDataFrame-GeoSeries.png)\n",
    "\n",
    "对于图中的index列是系统自行的加入的，用于对每一行进行唯一的索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>LINESTRING (13276175.345 3002670.696, 13275812...</td>\n",
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       "      <th>4</th>\n",
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       "      <td>LINESTRING (13276607.621 3003165.668, 13276482...</td>\n",
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       "      <th>5</th>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>LINESTRING (13275812.365 3002192.222, 13276488...</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>LINESTRING (13275653.974 3003020.476, 13275482...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   id                                           geometry\n",
       "0   1  LINESTRING (13275653.974 3003020.476, 13276175...\n",
       "1   2  LINESTRING (13276175.345 3002670.696, 13276607...\n",
       "2   3  LINESTRING (13276175.345 3002670.696, 13276878...\n",
       "3   4  LINESTRING (13276175.345 3002670.696, 13275812...\n",
       "4   5  LINESTRING (13276607.621 3003165.668, 13276482...\n",
       "5   6  LINESTRING (13276482.228 3003571.545, 13276914...\n",
       "6   7  LINESTRING (13275653.974 3003020.476, 13275980...\n",
       "7   8  LINESTRING (13275812.365 3002192.222, 13276488...\n",
       "8   9  LINESTRING (13275653.974 3003020.476, 13275482..."
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 导入geopandas并重新命名为gpd\n",
    "import geopandas as gpd\n",
    "# 读取数据\n",
    "gdf = gpd.read_file(\"./data/road.shp\")\n",
    "gdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 表示几何信息的`GeoSeries`对象\n",
    "> 在`Geopandas`中表示几何对象实际使用的是`Shapely`库的几何类型，其中包含`点线面、多点多线多面`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"9.0 9.0 2.0 2.0\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,20.0)\"><circle cx=\"10.0\" cy=\"10.0\" r=\"0.06\" stroke=\"#555555\" stroke-width=\"0.02\" fill=\"#66cc99\" opacity=\"0.6\" /></g></svg>"
      ],
      "text/plain": [
       "<POINT (10 10)>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from shapely.geometry import Point, LineString, Polygon, MultiPoint, MultiLineString, MultiPolygon\n",
    "# 点\n",
    "pt = Point(10,10)\n",
    "pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"10.96 10.96 1.0799999999999983 1.0799999999999983\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,23.0)\"><g><circle cx=\"11.0\" cy=\"11.0\" r=\"0.03239999999999995\" stroke=\"#555555\" stroke-width=\"0.010799999999999983\" fill=\"#66cc99\" opacity=\"0.6\" /><circle cx=\"12.0\" cy=\"12.0\" r=\"0.03239999999999995\" stroke=\"#555555\" stroke-width=\"0.010799999999999983\" fill=\"#66cc99\" opacity=\"0.6\" /></g></g></svg>"
      ],
      "text/plain": [
       "<MULTIPOINT (11 11, 12 12)>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 多点\n",
    "mpt = MultiPoint([(11,11), (12, 12)])\n",
    "mpt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"0.96 0.96 1.08 1.08\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,3.0)\"><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.0216\" points=\"1.0,1.0 2.0,2.0\" opacity=\"0.8\" /></g></svg>"
      ],
      "text/plain": [
       "<LINESTRING (1 1, 2 2)>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 线\n",
    "line = LineString([(1,1), (2,2)])\n",
    "line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"0.88 0.88 3.24 3.24\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,5.0)\"><g><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.06480000000000001\" points=\"1.0,1.0 2.0,2.0\" opacity=\"0.8\" /><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.06480000000000001\" points=\"3.0,3.0 4.0,4.0\" opacity=\"0.8\" /></g></g></svg>"
      ],
      "text/plain": [
       "<MULTILINESTRING ((1 1, 2 2), (3 3, 4 4))>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 多线\n",
    "mLine = MultiLineString([[(1,1),(2,2)], [(3,3),(4,4)]])\n",
    "mLine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-0.12 1.88 3.24 1.2400000000000002\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,5.0)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.06480000000000001\" opacity=\"0.6\" d=\"M 0.0,2.0 L 0.0,3.0 L 3.0,3.0 L 0.0,2.0 z\" /></g></svg>"
      ],
      "text/plain": [
       "<POLYGON ((0 2, 0 3, 3 3, 0 2))>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 面\n",
    "poly = Polygon([(0,2), (0,3), (3,3)])\n",
    "poly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-1.32 -0.32 8.64 6.640000000000001\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,6.0)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.1728\" opacity=\"0.6\" d=\"M -1.0,0.0 L 0.0,6.0 L 6.0,6.0 L 7.0,0.0 L -1.0,0.0 z M 0.0,3.0 L 4.0,5.0 L 6.0,1.0 L 0.0,3.0 z\" /></g></svg>"
      ],
      "text/plain": [
       "<POLYGON ((-1 0, 0 6, 6 6, 7 0, -1 0), (0 3, 4 5, 6 1, 0 3))>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 带有洞的面\n",
    "holesPoly = Polygon([(-1,0),(0,6),(6,6),(7,0)], holes=[[(0,3),(4,5),(6,1)]])\n",
    "holesPoly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-3.52 -3.52 14.04 10.04\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,2.9999999999999996)\"><g><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.2808\" opacity=\"0.6\" d=\"M -1.0,0.0 L 0.0,6.0 L 6.0,6.0 L 7.0,0.0 L -1.0,0.0 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.2808\" opacity=\"0.6\" d=\"M -2.0,-2.0 L -3.0,-3.0 L 10.0,0.0 L -2.0,-2.0 z\" /></g></g></svg>"
      ],
      "text/plain": [
       "<MULTIPOLYGON (((-1 0, 0 6, 6 6, 7 0, -1 0)), ((-2 -2, -3 -3, 10 0, -2 -2)))>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1 = Polygon([(-1,0),(0,6),(6,6),(7,0)])\n",
    "p2 = Polygon([(-2,-2), (-3,-3),(10,0)])\n",
    "mPoly = MultiPolygon([p1, p2])\n",
    "mPoly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      POLYGON ((-180.00000 90.00000, -165.00000 90.0...\n",
       "1      POLYGON ((-165.00000 90.00000, -150.00000 90.0...\n",
       "2      POLYGON ((-150.00000 90.00000, -135.00000 90.0...\n",
       "3      POLYGON ((-135.00000 90.00000, -120.00000 90.0...\n",
       "4      POLYGON ((-120.00000 90.00000, -105.00000 90.0...\n",
       "                             ...                        \n",
       "283    POLYGON ((105.00000 -75.00000, 120.00000 -75.0...\n",
       "284    POLYGON ((120.00000 -75.00000, 135.00000 -75.0...\n",
       "285    POLYGON ((135.00000 -75.00000, 150.00000 -75.0...\n",
       "286    POLYGON ((150.00000 -75.00000, 165.00000 -75.0...\n",
       "287    POLYGON ((165.00000 -75.00000, 180.00000 -75.0...\n",
       "Length: 288, dtype: geometry"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def grid(ns=(90,-90,-15),we=(-180,180,15) ):\n",
    "    #ns表示格网的北南的范围和纬度间隔，we表示格网的东西范围和经度间隔\n",
    "    grids=[]\n",
    "    latsep=ns[2]\n",
    "    lonsep=we[2]\n",
    "    for lat in range(*ns): # (90,-90,-15) 90~-90 每次步长为15\n",
    "        for lon in range(*we):\n",
    "            tmp=Polygon([ (lon,lat),(lon+lonsep,lat), (lon+lonsep,lat+latsep), (lon,lat+latsep) ])\n",
    "            grids.append(tmp)\n",
    "    return grids\n",
    "polys=grid()   #产生北纬90度至南纬90度间隔15度，西经180度到东经180度，间隔15度的一组多边形\n",
    "\n",
    "gs = gpd.GeoSeries(polys)\n",
    "gs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "gs.plot(color=(0,1,0,0.2), edgecolor=(0,0,0,1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 表示空间数据的`GeoDataFrame`对象\n",
    "> GeoDataFrame对象是类似一张表格，包含若干表示属性的列，和至少1列用于表示几何对象;在GIS的意义中，GeoDataFrame可以看作一个矢量图层，类似`geojson、shp...`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 如何构建GeoDataFrame\n",
    "# 1. 使用gpd.read_file读取的数据就是GeoDataFrame格式\n",
    "# 2. 文本数据构建\n",
    "parks=[{'name': '人民公园',\n",
    "  'addr': '成都市区青羊区祠堂街9号',\n",
    "  'tel': 'null',\n",
    "  'lon': 104.063684,\n",
    "  'lat': 30.663607},\n",
    " {'name': '青龙湖湿地公园',\n",
    "  'addr': '成都市龙泉驿区成洛大道',\n",
    "  'tel': 'null',\n",
    "  'lon': 104.199156,\n",
    "  'lat': 30.645796},\n",
    " {'name': '天府公园',\n",
    "  'addr': '杭州路东段与蜀州路交叉口西南150米',\n",
    "  'tel': 'null',\n",
    "  'lon': 104.085714,\n",
    "  'lat': 30.443121},\n",
    " {'name': '香草湖湿地公园',\n",
    "  'addr': '四川省成都市郫都区蜀源大道二段',\n",
    "  'tel': 'null',\n",
    "  'lon': 103.965884,\n",
    "  'lat': 30.844136},\n",
    " {'name': '麓湖生态城红石公园',\n",
    "  'addr': '成都市天府新区华阳街道天府大道南一段',\n",
    "  'tel': 'null',\n",
    "  'lon': 104.08512,\n",
    "  'lat': 30.466655},\n",
    " {'name': '东湖公园',\n",
    "  'addr': '成都市锦江区二环路东五段299号',\n",
    "  'tel': 'null',\n",
    "  'lon': 104.094487,\n",
    "  'lat': 30.621803},\n",
    " {'name': '新华公园',\n",
    "  'addr': '四川省成都市成华区双林路87号',\n",
    "  'tel': '(028)84382816',\n",
    "  'lon': 104.112282,\n",
    "  'lat': 30.662277},\n",
    " {'name': '清水河生态艺术公园',\n",
    "  'addr': '四川省成都市郫都区郫温路',\n",
    "  'tel': 'null',\n",
    "  'lon': 103.860947,\n",
    "  'lat': 30.799324}]\n",
    "\n",
    "geoms = []\n",
    "for park in parks:\n",
    "    pt = Point(park['lon'], park['lat'])\n",
    "    geoms.append(pt)\n",
    "\n",
    "\n",
    "parkslayer = gpd.GeoDataFrame(parks, geometry=geoms)\n",
    "parkslayer.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `GeoDataFrame`增删改查"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
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     },
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     "output_type": "display_data"
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     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>addr</th>\n",
       "      <th>tel</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>geometry</th>\n",
       "      <th>newSerise</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>人民公园</td>\n",
       "      <td>成都市区青羊区祠堂街9号</td>\n",
       "      <td>null</td>\n",
       "      <td>104.063684</td>\n",
       "      <td>30.663607</td>\n",
       "      <td>POINT (104.06368 30.66361)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>青龙湖湿地公园</td>\n",
       "      <td>成都市龙泉驿区成洛大道</td>\n",
       "      <td>null</td>\n",
       "      <td>104.199156</td>\n",
       "      <td>30.645796</td>\n",
       "      <td>POINT (104.19916 30.64580)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>天府公园</td>\n",
       "      <td>杭州路东段与蜀州路交叉口西南150米</td>\n",
       "      <td>null</td>\n",
       "      <td>104.085714</td>\n",
       "      <td>30.443121</td>\n",
       "      <td>POINT (104.08571 30.44312)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>香草湖湿地公园</td>\n",
       "      <td>四川省成都市郫都区蜀源大道二段</td>\n",
       "      <td>null</td>\n",
       "      <td>103.965884</td>\n",
       "      <td>30.844136</td>\n",
       "      <td>POINT (103.96588 30.84414)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>麓湖生态城红石公园</td>\n",
       "      <td>成都市天府新区华阳街道天府大道南一段</td>\n",
       "      <td>null</td>\n",
       "      <td>104.085120</td>\n",
       "      <td>30.466655</td>\n",
       "      <td>POINT (104.08512 30.46665)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>东湖公园</td>\n",
       "      <td>成都市锦江区二环路东五段299号</td>\n",
       "      <td>null</td>\n",
       "      <td>104.094487</td>\n",
       "      <td>30.621803</td>\n",
       "      <td>POINT (104.09449 30.62180)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>新华公园</td>\n",
       "      <td>四川省成都市成华区双林路87号</td>\n",
       "      <td>(028)84382816</td>\n",
       "      <td>104.112282</td>\n",
       "      <td>30.662277</td>\n",
       "      <td>POINT (104.11228 30.66228)</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>清水河生态艺术公园</td>\n",
       "      <td>四川省成都市郫都区郫温路</td>\n",
       "      <td>null</td>\n",
       "      <td>103.860947</td>\n",
       "      <td>30.799324</td>\n",
       "      <td>POINT (103.86095 30.79932)</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>维也纳森林公园</td>\n",
       "      <td>成都市双流区滨河路三段</td>\n",
       "      <td>null</td>\n",
       "      <td>104.063755</td>\n",
       "      <td>30.528549</td>\n",
       "      <td>POINT (104.06376 30.52855)</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name                addr            tel         lon        lat  \\\n",
       "0       人民公园        成都市区青羊区祠堂街9号           null  104.063684  30.663607   \n",
       "1    青龙湖湿地公园         成都市龙泉驿区成洛大道           null  104.199156  30.645796   \n",
       "2       天府公园  杭州路东段与蜀州路交叉口西南150米           null  104.085714  30.443121   \n",
       "3    香草湖湿地公园     四川省成都市郫都区蜀源大道二段           null  103.965884  30.844136   \n",
       "4  麓湖生态城红石公园  成都市天府新区华阳街道天府大道南一段           null  104.085120  30.466655   \n",
       "5       东湖公园    成都市锦江区二环路东五段299号           null  104.094487  30.621803   \n",
       "6       新华公园     四川省成都市成华区双林路87号  (028)84382816  104.112282  30.662277   \n",
       "7  清水河生态艺术公园        四川省成都市郫都区郫温路           null  103.860947  30.799324   \n",
       "8    维也纳森林公园         成都市双流区滨河路三段           null  104.063755  30.528549   \n",
       "\n",
       "                     geometry  newSerise  \n",
       "0  POINT (104.06368 30.66361)          0  \n",
       "1  POINT (104.19916 30.64580)          1  \n",
       "2  POINT (104.08571 30.44312)          2  \n",
       "3  POINT (103.96588 30.84414)          3  \n",
       "4  POINT (104.08512 30.46665)          4  \n",
       "5  POINT (104.09449 30.62180)          5  \n",
       "6  POINT (104.11228 30.66228)          6  \n",
       "7  POINT (103.86095 30.79932)          7  \n",
       "8  POINT (104.06376 30.52855)          8  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newpark= {'name': '维也纳森林公园',\n",
    " 'addr': '成都市双流区滨河路三段',\n",
    " 'tel': 'null',\n",
    " 'lon': 104.063755,\n",
    " 'lat': 30.528549}\n",
    "\n",
    "newGeom = Point(newpark['lon'], newpark['lat'])\n",
    "newpark['geometry'] = newGeom\n",
    "parkslayer = parkslayer._append([newpark], ignore_index=True)\n",
    "parkslayer.plot()\n",
    "plt.show()\n",
    "\n",
    "# 新增一列\n",
    "parkslayer['newSerise'] = range(len(parkslayer))\n",
    "parkslayer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>addr</th>\n",
       "      <th>tel</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>人民公园</td>\n",
       "      <td>成都市区青羊区祠堂街9号</td>\n",
       "      <td>null</td>\n",
       "      <td>104.063684</td>\n",
       "      <td>30.663607</td>\n",
       "      <td>POINT (104.06368 30.66361)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>青龙湖湿地公园</td>\n",
       "      <td>成都市龙泉驿区成洛大道</td>\n",
       "      <td>null</td>\n",
       "      <td>104.199156</td>\n",
       "      <td>30.645796</td>\n",
       "      <td>POINT (104.19916 30.64580)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>天府公园</td>\n",
       "      <td>杭州路东段与蜀州路交叉口西南150米</td>\n",
       "      <td>null</td>\n",
       "      <td>104.085714</td>\n",
       "      <td>30.443121</td>\n",
       "      <td>POINT (104.08571 30.44312)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>香草湖湿地公园</td>\n",
       "      <td>四川省成都市郫都区蜀源大道二段</td>\n",
       "      <td>null</td>\n",
       "      <td>103.965884</td>\n",
       "      <td>30.844136</td>\n",
       "      <td>POINT (103.96588 30.84414)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>麓湖生态城红石公园</td>\n",
       "      <td>成都市天府新区华阳街道天府大道南一段</td>\n",
       "      <td>null</td>\n",
       "      <td>104.085120</td>\n",
       "      <td>30.466655</td>\n",
       "      <td>POINT (104.08512 30.46665)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>东湖公园</td>\n",
       "      <td>成都市锦江区二环路东五段299号</td>\n",
       "      <td>null</td>\n",
       "      <td>104.094487</td>\n",
       "      <td>30.621803</td>\n",
       "      <td>POINT (104.09449 30.62180)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>新华公园</td>\n",
       "      <td>四川省成都市成华区双林路87号</td>\n",
       "      <td>(028)84382816</td>\n",
       "      <td>104.112282</td>\n",
       "      <td>30.662277</td>\n",
       "      <td>POINT (104.11228 30.66228)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>清水河生态艺术公园</td>\n",
       "      <td>四川省成都市郫都区郫温路</td>\n",
       "      <td>null</td>\n",
       "      <td>103.860947</td>\n",
       "      <td>30.799324</td>\n",
       "      <td>POINT (103.86095 30.79932)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>维也纳森林公园</td>\n",
       "      <td>成都市双流区滨河路三段</td>\n",
       "      <td>null</td>\n",
       "      <td>104.063755</td>\n",
       "      <td>30.528549</td>\n",
       "      <td>POINT (104.06376 30.52855)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name                addr            tel         lon        lat  \\\n",
       "0       人民公园        成都市区青羊区祠堂街9号           null  104.063684  30.663607   \n",
       "1    青龙湖湿地公园         成都市龙泉驿区成洛大道           null  104.199156  30.645796   \n",
       "2       天府公园  杭州路东段与蜀州路交叉口西南150米           null  104.085714  30.443121   \n",
       "3    香草湖湿地公园     四川省成都市郫都区蜀源大道二段           null  103.965884  30.844136   \n",
       "4  麓湖生态城红石公园  成都市天府新区华阳街道天府大道南一段           null  104.085120  30.466655   \n",
       "5       东湖公园    成都市锦江区二环路东五段299号           null  104.094487  30.621803   \n",
       "6       新华公园     四川省成都市成华区双林路87号  (028)84382816  104.112282  30.662277   \n",
       "7  清水河生态艺术公园        四川省成都市郫都区郫温路           null  103.860947  30.799324   \n",
       "8    维也纳森林公园         成都市双流区滨河路三段           null  104.063755  30.528549   \n",
       "\n",
       "                     geometry  \n",
       "0  POINT (104.06368 30.66361)  \n",
       "1  POINT (104.19916 30.64580)  \n",
       "2  POINT (104.08571 30.44312)  \n",
       "3  POINT (103.96588 30.84414)  \n",
       "4  POINT (104.08512 30.46665)  \n",
       "5  POINT (104.09449 30.62180)  \n",
       "6  POINT (104.11228 30.66228)  \n",
       "7  POINT (103.86095 30.79932)  \n",
       "8  POINT (104.06376 30.52855)  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除\n",
    "# 1. 指定索引的数据\n",
    "parkslayer1 = parkslayer.drop([0,3])\n",
    "parkslayer1\n",
    "\n",
    "# 删除一列\n",
    "parkslayer2 = parkslayer.drop(columns=['newSerise'])\n",
    "parkslayer2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>addr</th>\n",
       "      <th>tel</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>geometry</th>\n",
       "      <th>newSerise</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>人民公园</td>\n",
       "      <td>新修改的地址</td>\n",
       "      <td>null</td>\n",
       "      <td>104.063684</td>\n",
       "      <td>30.663607</td>\n",
       "      <td>POINT (104.06368 30.66361)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>青龙湖湿地公园</td>\n",
       "      <td>成都市龙泉驿区成洛大道</td>\n",
       "      <td>null</td>\n",
       "      <td>104.199156</td>\n",
       "      <td>30.645796</td>\n",
       "      <td>POINT (104.19916 30.64580)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>天府公园</td>\n",
       "      <td>杭州路东段与蜀州路交叉口西南150米</td>\n",
       "      <td>null</td>\n",
       "      <td>104.085714</td>\n",
       "      <td>30.443121</td>\n",
       "      <td>POINT (104.08571 30.44312)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>香草湖湿地公园</td>\n",
       "      <td>四川省成都市郫都区蜀源大道二段</td>\n",
       "      <td>null</td>\n",
       "      <td>103.965884</td>\n",
       "      <td>30.844136</td>\n",
       "      <td>POINT (103.96588 30.84414)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>麓湖生态城红石公园</td>\n",
       "      <td>成都市天府新区华阳街道天府大道南一段</td>\n",
       "      <td>null</td>\n",
       "      <td>104.085120</td>\n",
       "      <td>30.466655</td>\n",
       "      <td>POINT (104.08512 30.46665)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>东湖公园</td>\n",
       "      <td>成都市锦江区二环路东五段299号</td>\n",
       "      <td>null</td>\n",
       "      <td>104.094487</td>\n",
       "      <td>30.621803</td>\n",
       "      <td>POINT (104.09449 30.62180)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>新华公园</td>\n",
       "      <td>四川省成都市成华区双林路87号</td>\n",
       "      <td>(028)84382816</td>\n",
       "      <td>104.112282</td>\n",
       "      <td>30.662277</td>\n",
       "      <td>POINT (104.11228 30.66228)</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>清水河生态艺术公园</td>\n",
       "      <td>四川省成都市郫都区郫温路</td>\n",
       "      <td>null</td>\n",
       "      <td>103.860947</td>\n",
       "      <td>30.799324</td>\n",
       "      <td>POINT (103.86095 30.79932)</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>维也纳森林公园</td>\n",
       "      <td>成都市双流区滨河路三段</td>\n",
       "      <td>null</td>\n",
       "      <td>104.063755</td>\n",
       "      <td>30.528549</td>\n",
       "      <td>POINT (104.06376 30.52855)</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name                addr            tel         lon        lat  \\\n",
       "0       人民公园              新修改的地址           null  104.063684  30.663607   \n",
       "1    青龙湖湿地公园         成都市龙泉驿区成洛大道           null  104.199156  30.645796   \n",
       "2       天府公园  杭州路东段与蜀州路交叉口西南150米           null  104.085714  30.443121   \n",
       "3    香草湖湿地公园     四川省成都市郫都区蜀源大道二段           null  103.965884  30.844136   \n",
       "4  麓湖生态城红石公园  成都市天府新区华阳街道天府大道南一段           null  104.085120  30.466655   \n",
       "5       东湖公园    成都市锦江区二环路东五段299号           null  104.094487  30.621803   \n",
       "6       新华公园     四川省成都市成华区双林路87号  (028)84382816  104.112282  30.662277   \n",
       "7  清水河生态艺术公园        四川省成都市郫都区郫温路           null  103.860947  30.799324   \n",
       "8    维也纳森林公园         成都市双流区滨河路三段           null  104.063755  30.528549   \n",
       "\n",
       "                     geometry  newSerise  \n",
       "0  POINT (104.06368 30.66361)          0  \n",
       "1  POINT (104.19916 30.64580)          1  \n",
       "2  POINT (104.08571 30.44312)          2  \n",
       "3  POINT (103.96588 30.84414)          3  \n",
       "4  POINT (104.08512 30.46665)          4  \n",
       "5  POINT (104.09449 30.62180)          5  \n",
       "6  POINT (104.11228 30.66228)          6  \n",
       "7  POINT (103.86095 30.79932)          7  \n",
       "8  POINT (104.06376 30.52855)          8  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 修改数据\n",
    "parkslayer.loc[0, \"addr\"] = '新修改的地址'\n",
    "parkslayer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 空间数据简单的可视化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> GeoSeries 和 GeoDataFrame 都拥有可以用于可视化的 plot() 方法。plot() 方法返回的是一个 ax 对象（也就是绘图区域），我们可以将多个图层绘制在同一个图上，从而实现图层的叠加显示。在绘图完成后，调用matplotlib库中的 pyplot() 方法（一般重命名为plt）在notebook中进行显示。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parkslayer.plot(markersize=90, color=(0,1,0,0.2),edgecolor=(0,0,0,1), linewidth=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parkslayer.plot(column='newSerise',categorical=True, legend=True, cmap='gist_rainbow', legend_kwds={'loc': 'center left', 'bbox_to_anchor':(1,0.5)})\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pygis",
   "language": "python",
   "name": "pygis"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
